The concepts involved in the present invention relate to automated techniques for qualifying subscriber loops for digital subscriber line (DSL) services, based on the learning capabilities of an expert system. Preferably, the inventive techniques classify loops by prediction of performance metrics indicating that loop performance falls within ranges of data rate corresponding to different xDSL service grades offered by a carrier or other service provider.
Modern society continues to create exponentially increasing demands for digital information, and the communication of such information creates increasing needs for ever-faster data communication speeds. To meet the demand for speed, a number of technologies are being developed and are in early stages of deployment, for providing substantially higher rates of data communication, for example ranging from 640 kb/s to several Mb/s. In particular, a number of the local telephone carriers are working on enhancements to their existing copper-wire loop networks, based on various xDSL technologies. xDSL here is used as a generic term for a group of higher-rate digital subscriber line communication schemes capable of utilizing twisted pair wiring from an office or other terminal node of a telephone network to the subscriber premises. Examples under various stages of development include ADSL (Asymmetrical Digital Subscriber Line), HDSL (High data rate Digital Subscriber Line) and VDSL (Very high data rate Digital Subscriber Line). As one example, ADSL modems today are typically providing downstream data rates in ranges of 640 kb/s, 1.6 Mb/s and 7.1 Mb/s.
Installation, operating and maintenance of xDSL data services, however, pose a number of problems. These problems may be particularly acute where a carrier is considering upgrading to an xDSL service on an existing subscriber""s line circuit. The precise data rate of any xDSL service depends on many factors, such as line length, copper wire gauge, cross-coupled interference, and the like. As a general rule, the shorter the distance and/or the larger the diameter of the wire (smaller the gauge), the higher the rate can be on the particular telephone line. If the wiring has been in place and used for Plain Old Telephone Service (POTS) there may be load coils on the line, which prevent xDSL services. Bridged-taps, which are common in telephone loop plant, also cause severe performance problems.
To provide service to a customer seeking to upgrade to an xDSL service, the carrier must determine if the loop to that customer""s premises can support the desired xDSL grade of service, and if not, what lower rate service the loop might support. Loop Qualification refers to the task of pre-determining the data rate capacities of loops for high-speed services. For example, a current ADSL Loop Qualification process may focus on which one of three service ranges a customer""s loop can support. The three service grades refer to ranges of data described by upper limits of 640 kb/s, 1.6 Mb/s or 7.1 Mbps. Customers are charged based on which range data rate range they choose, predicated on the loop""s ability to support it. The fourth possibility, however, is that the loop cannot support any DSL service.
Loop qualification often relies on parameters culled from existing databases regarding cable make-ups of a carrier""s outside loop plant. Cable make-up refers to information such as wire gauges, loop lengths and load coils. Sources of cable make-up information include legacy databases and test systems. These sources, however, are notorious for containing incomplete and erroneous information. Further, the quality of databases vary from region to region, telephone company to telephone company and even from wire-center to wire-center. The problems with such information is further increased by the spate of mergers involving regional telephone companies, particularly when each party to the merger has its own legacy and test systems with varying degrees of accuracy and completeness.
The Loop Qualification process to this point has focused primarily on human expert experience in quantifying loop capacity, using established criteria for determining data rates. Some Loop Qualification processes, for example, rely primarily on manually weeding out obvious high power interference sources located in the same binder group, such as T1 disturbers. In most other cases, loop qualification is based solely on the distance of the customer from the CO. The.process is intensely manual and often requires many truck rolls to confirm or repair service. Due to the inaccuracies or incompleteness of the data used or the limitations of the human expert, the current qualification processes produce two basic types of errors.
Type A errors occur when the carrier qualifies a loop, certifying that it can support a desired grade of service, but when actually used by the subscriber, the loop does not support that grade of service. This generates a complaint by the customer, and the carrier often will send personnel to try to test the line and fix the problem. Sometimes, repairs are possible but require considerable labor. At other times, the repairs may not even be feasible. The subscriber, of course, will not pay for the service that the carrier fails to deliver. The attempts to make manual repairs incur considerable expense, in terms of truck rolls, even if the repairs ultimately fail to deliver the service that the carrier stated that the line could deliver.
The other type of errors, Type B, occur when the carrier decides that a line will not support a desired grade of service, when in fact it could. In this case, the subscriber may obtain a lower grade service over the line, but of course, the subscriber will pay a lower rate than would otherwise have been the case. In many instances, the qualification process may indicate that the line will not support any kind of DSL service at all, in which case the carrier completely loses the opportunity to sell a subscription for such a service to the would-be customer. All such errors essentially cost the carrier opportunities to sell a service or to sell an even higher grade of service and result in lost revenue.
Hence, both errors result in quantifiable economic costs that are borne by the exchange carrier. Failure to mitigate these errors results in reduced revenues and increased expenses. More specifically, Type A errors result in increased expenses due to the dispatch of unnecessary truck rolls. Type B errors translate into lost revenue. Both error types lead to a reduced profitability of xDSL services. Additional expenses also accrue, directly and indirectly, from the increased xe2x80x98bad willxe2x80x99 of customers towards the carrier, an increase in unsatisfied customers, greater scrutiny by regulatory agencies, and an eroded corporate image. Other service offerings may also suffer reduced profitability since customer dissatisfaction with xDSL could lead to disconnects by dissatisfied customers who then seek broadband and other services from competitors, and so lead to a hemorrhaging of current revenue and profit streams or the DSL carrier.
Predictive models have been developed in a first effort to automate the task of predicting the level of service that a loop might support. Any such model is only a first order approximation of reality and is only as good as the assumptions incorporated into the model. For example, several existing models predict service level or throughput for xDSL service as a function of loop length. For lines under otherwise equal conditions, these models adequately approximate the values of performance metrics as a function of the variable loop length. However, such models can not account for other variable conditions, particularly locally unique conditions, that may effect xDSL performance. For example, two lines of the same overall length may support radically different levels of xDSL service. One may support a high-rate service, and the other may not support any DSL service, because of differences in the bridged tap conditions of the two lines. If the model does not include bridged tap as a parameter, the model will not accurately predict this difference.
Also, with deterministic models it is not really possible to account for variables that in many cases may not be specifically known, such as different levels of exposure to localized sources of external interference. Deterministic models simply can not deal with effects of parameters that are not specifically included in the deterministic algorithms.
Finally, the deterministic models can not deal with inaccurate or incomplete data. Because the cable make-up information from a carrier""s existing legacy systems often is incomplete or inaccurate, there is no way to insure that the deterministic model can provide a prediction or that the prediction will ever be relatively accurate.
Hence, while the deterministic models do allow automation, they have helped little to reduce the instances of Type A and Type B errors.
It is an objective of this invention to automate the loop qualification process. A further objective is to automate the loop qualification process in such a manner as to reduce or eliminate errors, for example, the errors caused by inaccurate or incomplete data in a carrier""s loop-plant data records or produced by existing test systems. Any such system or methodology should be capable of providing accurate qualification with relatively little data and/or in spite of errors in existing data. Also, the loop qualification technique should be adaptive, as the system receives new information regarding actual lines in service and/or relating to changes in outside-plant conditions.
The invention achieves the above stated objects and overcomes the noted problems in the art through the use of an expert system, typically based on neural networks.
An xe2x80x98expert systemxe2x80x99 for purposes of discussion here is a computer program, which compiles a set of general rules or algorithmic statements from analysis of a database of known inputs and outputs. The expert system runs a logical engine to apply a given set of facts, about a new input, to the statements learned from the database to predict one or more new outputs. An expert system is able to account for unknown or hidden type input parameters, that is to say data that affects results that are not explicitly recognized. Also, an expert system adjusts the algorithm(s) based on experience to improve its performance. The expert system requires neither a physical or prescribed relationship between the inputs and outputs. The expert system approximates relationships in algorithms, but a person need not recognize or ever understand any of those relationships.
In accord with the invention, a database is built of information characterizing loops providing xDSL service and information regarding the performance of those loops. The database includes data for loops carrying the various levels of DSL service available in the network and preferably some data regarding loops that the carrier has found can not support any of the available levels of DSL service. The expert system xe2x80x98learnsxe2x80x99 from this operational database to develop and maintain a set of internal statements for predicting performance. Essentially, the expert system correlates the in-service loop characteristic data to the performance data for the in-service loop to develop the predictive statements. Then, in response to data characterizing a new loop for which an xDSL grade of service is requested, the expert system predicts a level of performance using its most current set of internal statements.
If the new loop qualifies for an xDSL grade of service, the carrier can place that loop in service. Subsequently, the carrier obtains performance data for the actual service on that loop and adds the characteristic data and performance data for that loop to the operational database of the loop qualification system.
Aspects of the invention relate to systems and methods for qualifying a loop with regard to digital subscriber line service, using the inventive expert system approach. Other aspects of the invention relate to software products, operation of which in a computer enables the computer to qualify a loop with regard to digital subscriber line service.
For example, one aspect of the invention relates to a system for qualifying a loop with regard to digital subscriber line service in a network providing a plurality of levels of digital subscriber line service over a plurality of loops. The system includes a database of records regarding loops in service in a predefined section of the network. Each record for an in-service loop includes characteristic data regarding the loop and performance data regarding capability of operation of a digital subscriber line service over that loop. The loop qualifying system also comprises an expert system coupled to the database. The expert system learns statements by correlating the predetermined characteristic data to the known performance data. A source provides an input to the expert system of at least some characteristic data regarding a loop to be qualified. In response, the expert system applies the learned statements to the input characteristic data, to develop a performance prediction for the loop being qualified.
The preferred embodiments of the invention incorporate the information about the cable make-up of each loop that is available from several legacy databases and test heads. In the preferred embodiment, the expert system uses a neural network program, for example implementing a genetic algorithm. The use of such expert systems increases the number of parameters that can be used in determining loop qualification, improving accuracy and system robustness. Also, such an approach takes advantage of the ongoing ability of expert systems to xe2x80x98learnxe2x80x99 based on new data input, and the ability of such systems to accept noisy data. Furthermore, expert systems can accept data of varying degrees of completeness and yet remain relatively fault tolerant, recovering from the transfer of inaccurate elements of data, a prevalent feature in telephone company legacy and test systems. Consequently, the system will reduce the occurrence of Type A and Type B errors.
Though the following description concentrates on the use of this system in determining grades of service, it is equally applicable for determining other metrics related to the performance or the quality of service (Qos) of a carrier""s network.
The preferred embodiment of a system implementing the invention includes a database containing the cable make-ups of xDSL lines in operation and the highest grade of service for which each in-service line qualified. For example, the database may, depending on availability and accuracy at each location, consist of operational parameters such as loop gauge, length, and location, length and gauge of bridged taps, binder group identification, and services offered in the same or adjacent binder groups. As each new loop is evaluated for qualification, the available cable-make up for that loop will be loaded into the system. As noted before, data will be obtained from the appropriate sources including legacy databases and test-heads. Practically, the different degrees of accuracy and the different types of systems available throughout a telephone company will probably dictate that data for different loops will be obtained from different sources.
The initial data set, constantly expanding as more lines are introduced, is accessible to the expert system from the database. The expert system will use this initial data set to xe2x80x98learnxe2x80x99 about the network setup. As new cable make-ups are entered, for qualification purposes, the expert system will examine the inputs and adjust its internal weightsxe2x80x94either directly or through hidden layersxe2x80x94and produce the xe2x80x98bestxe2x80x99 output prediction for grade of service for each subsequent line. For example, for a network application supporting three grades of xDSL service, the expert system would classify new loops for the three levels or for no DSL service possible. The expert system can be expanded to output xe2x80x98Nxe2x80x99 possibilities. The xe2x80x98bestxe2x80x99 output prediction is based on standard statistical practices, such as the least root mean square error or highest correlation.
As monitoring systems examining actual performance are introduced into the network and/or customer complaints are received, the database is adjusted to reflect increased knowledge of the cable make-up and grade of service actually achieved. Each new xDSL correction provides the expert system neural network an opportunity to xe2x80x98re-learnxe2x80x99 the carrier""s loop plant. xe2x80x98Re-Learningxe2x80x99, or more practically the adjustments of weights within the neural network, compensates for instances in which a loop is qualified at a particular speed and is found to operate at a higher speed (Type B error) or lower speed (Type A error). As a neural network can use an initial functional form, in the hidden layers of the expert system, to optimize its performance, appropriate predictive, deterministic models will be selected to provide an initial performance prediction. The model will be selected based on the available information in the cable make-up and will be adjusted as additional information is provided.
This system is equally useful in the implementation or planning environments. Depending on need and other constraints, the system can be implemented on a wire-center basisxe2x80x94that is the expert system is located or virtually connected to the COxe2x80x94or in a more centralized location. In the centralized scenario, the central computer will download the necessary performance and cable make up data from several COs and the appropriate test and monitoring systems.
Additional objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.